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      Practical selection of SVM parameters and noise estimation for SVM regression.

      Neural Networks
      Bayes Theorem, Models, Theoretical, Nonlinear Dynamics, Normal Distribution, Regression Analysis, Reproducibility of Results

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          Abstract

          We investigate practical selection of hyper-parameters for support vector machines (SVM) regression (that is, epsilon-insensitive zone and regularization parameter C). The proposed methodology advocates analytic parameter selection directly from the training data, rather than re-sampling approaches commonly used in SVM applications. In particular, we describe a new analytical prescription for setting the value of insensitive zone epsilon, as a function of training sample size. Good generalization performance of the proposed parameter selection is demonstrated empirically using several low- and high-dimensional regression problems. Further, we point out the importance of Vapnik's epsilon-insensitive loss for regression problems with finite samples. To this end, we compare generalization performance of SVM regression (using proposed selection of epsilon-values) with regression using 'least-modulus' loss (epsilon=0) and standard squared loss. These comparisons indicate superior generalization performance of SVM regression under sparse sample settings, for various types of additive noise.

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          Author and article information

          Journal
          14690712
          10.1016/S0893-6080(03)00169-2

          Chemistry
          Bayes Theorem,Models, Theoretical,Nonlinear Dynamics,Normal Distribution,Regression Analysis,Reproducibility of Results

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